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Soft and Wearable Sensors for Human Health Monitoring
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Soft and Wearable Sensors for Human Health Monitoring

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".

Deadline for manuscript submissions: closed (25 December 2024) | Viewed by 2953

Special Issue Editor

Dr. Yongkuk Lee

E-Mail Website
Guest Editor
Department of Biomedical Engineering, Wichita State University, Wichita, KS 67260, USA
Interests: flexible electronics; stretchable electronics; wearable electronics; health monitoring; nanomanufacturing; human–machine interfaces
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Soft and wearable sensors have received great attention for human health monitoring in recent years. Soft and wearable sensors are deformable, biocompatible, and lightweight, while retaining the functionalities of conventional sensors. They can be comfortable and user-friendly and offer long-term, continuous health monitoring with high signal fidelity. They can help to overcome the limitations of current biomedical devices for the early screening/diagnosis, prevention, and management of diseases and they can enhance the quality and independence of human life. Combined with artificial intelligence technologies, including machine learning and deep learning, they can also offer an automated analysis of diseases in real-time to realize home-based and patient-centered healthcare. However, there are great challenges in the development of such advanced soft and wearable sensors, requiring interdisciplinary studies and experiments involving engineering, chemistry, material sciences, analysis, and data science.

This Special Issue will focus on discussions and insights regarding the current state-of-the-art and challenges in the development of soft and wearable sensors. It will include not only novel strategies for the design, materials, manufacturing, and fabrication of soft and wearable sensors but also their potential applications for health monitoring.

In this context, topics of interest include, but are not limited to, the following:

  • Soft and wearable sensors and electronics for health monitoring;
  • Advanced materials and manufacturing for soft and wearable sensors;
  • Wearable/implantable sensors for healthcare applications;
  • Sensors for continuous, long-term monitoring;
  • Bio-integrated/stretchable electronics;
  • AI-enabled health monitoring with wearable sensors.

Dr. Yongkuk Lee
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • soft and wearable sensors and electronics for health monitoring
  • advanced materials and manufacturing for soft and wearable sensors
  • wearable/implantable sensors for healthcare applications
  • sensors for continuous, long-term monitoring
  • bio-integrated/stretchable electronics
  • AI-enabled health monitoring with wearable sensors.

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Published Papers (2 papers)

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16 pages, 2546 KiB  
Article
Evaluation of the ActiMotus Software to Accurately Classify Postures and Movements in Children Aged 3–14
by Charlotte Lund Rasmussen, Danica Hendry, George Thomas, Amber Beynon, Sarah Michelle Stearne, Juliana Zabatiero, Paul Davey, Jon Roslyng Larsen, Andrew Lloyd Rohl, Leon Straker and Amity Campbell
Sensors 2024, 24(20), 6705; https://doi.org/10.3390/s24206705 - 18 Oct 2024
Viewed by 1104
Abstract
Background: ActiMotus, a thigh-accelerometer-based software used for the classification of postures and movements (PaMs), has shown high accuracy among adults and school-aged children; however, its accuracy among younger children and potential differences between sexes are unknown. This study aimed to evaluate the accuracy [...] Read more.
Background: ActiMotus, a thigh-accelerometer-based software used for the classification of postures and movements (PaMs), has shown high accuracy among adults and school-aged children; however, its accuracy among younger children and potential differences between sexes are unknown. This study aimed to evaluate the accuracy of ActiMotus to measure PaMs among children between 3 and 14 years and to assess if this was influenced by the sex or age of children. Method: Forty-eight children attended a structured ~1-hour data collection session at a laboratory. Thigh acceleration was measured using a SENS accelerometer, which was classified into nine PaMs using the ActiMotus software. Human-coded video recordings of the session provided the ground truth. Results: Based on both F1 scores and balanced accuracy, the highest levels of accuracy were found for lying, sitting, and standing (63.2–88.2%). For walking and running, accuracy measures ranged from 48.0 to 85.8%. The lowest accuracy was observed for classifying stair climbing. We found a higher accuracy for stair climbing among girls compared to boys and for older compared to younger age groups for walking, running, and stair climbing. Conclusions: ActiMotus could accurately detect lying, sitting, and standing among children. The software could be improved for classifying walking, running, and stair climbing, particularly among younger children. Full article
(This article belongs to the Special Issue Soft and Wearable Sensors for Human Health Monitoring)
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18 pages, 3200 KiB  
Article
Human Activity Recording Based on Skin-Strain-Actuated Microfluidic Pumping in Asymmetrically Designed Micro-Channels
by Caroline Barbar Askar, Nick Cmager, Rana Altay and I. Emre Araci
Sensors 2024, 24(13), 4207; https://doi.org/10.3390/s24134207 - 28 Jun 2024
Cited by 1 | Viewed by 1398
Abstract
The capability to record data in passive, image-based wearable sensors can simplify data readouts and eliminate the requirement for the integration of electronic components on the skin. Here, we developed a skin-strain-actuated microfluidic pump (SAMP) that utilizes asymmetric aspect ratio channels for the [...] Read more.
The capability to record data in passive, image-based wearable sensors can simplify data readouts and eliminate the requirement for the integration of electronic components on the skin. Here, we developed a skin-strain-actuated microfluidic pump (SAMP) that utilizes asymmetric aspect ratio channels for the recording of human activity in the fluidic domain. An analytical model describing the SAMP’s operation mechanism as a wearable microfluidic device was established. Fabrication of the SAMP was achieved using soft lithography from polydimethylsiloxane (PDMS). Benchtop experimental results and theoretical predictions were shown to be in good agreement. The SAMP was mounted on human skin and experiments conducted on volunteer subjects demonstrated the SAMP’s capability to record human activity for hundreds of cycles in the fluidic domain through the observation of a stable liquid meniscus. Proof-of-concept experiments further revealed that the SAMP could quantify a single wrist activity repetition or distinguish between three different shoulder activities. Full article
(This article belongs to the Special Issue Soft and Wearable Sensors for Human Health Monitoring)
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